# -*- coding: utf-8 -*-
"""
Created on Fri Oct 20 20:55:37 2017

@author: yang
"""
import os#
import utils#
import matplotlib.image as mpimg#
import matplotlib.pyplot as plt#
import numpy as np
import cv2#
import glob#
import time#
from sklearn.svm import SVC,LinearSVC#
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
from skimage.feature import hog############################
from sklearn.externals import joblib
import pickle#
from sklearn.grid_search import GridSearchCV
# Divide up into cars and notcars


notcars = glob.glob('../non-vehicles/*/*.png')
cars = glob.glob('../vehicles/*/*.png')



# Reduce the sample size because HOG features are slow to compute
# The quiz evaluator times out after 13s of CPU time
#sample_size = 500
#cars = cars[0:sample_size]
#notcars = notcars[0:sample_size]

### TODO: Tweak these parameters and see how the results change.
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

t = time.time()
car_features = utils.extract_features(cars, cspace=colorspace, orient=orient,
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
                        hog_channel=hog_channel)
notcar_features = utils.extract_features(notcars, cspace=colorspace, orient=orient,
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
                        hog_channel=hog_channel)

t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract features...')

# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features))
X = X.astype(np.float64)                       
# Fit a per-column scaler
# X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
# scaled_X = X_scaler.transform(X)

# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))


# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=rand_state)


print('Feature vector length:', len(X_train[0]))
# Use a linear SVC 
svc = LinearSVC()
# Check the training time for the SVC
t = time.time()
svc.fit(X_train, y_train)
t2 = time.time()
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train classfier...')
# Check the score of the SVC
print('Test Accuracy of classfier = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My classfier predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with classfier')


train_dist={}
train_dist['clf']=svc
train_dist['scaler']=None
train_dist['orient']=orient
train_dist['pix_per_cell'] = pix_per_cell
train_dist['cell_per_block'] = cell_per_block
train_dist['hog_channel'] = hog_channel
train_dist['spatial_size'] = None
train_dist['hist_bins'] = None

output = open('train_dist.p', 'wb')
pickle.dump(train_dist,output)

